# -*- coding: utf-8 -*-
# __init__.py create by kumiler
# on 2022/9/22 14:30
# desc

from datetime import timedelta

from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from spmi.ods.mysql.spmi import spmi_ods__spmn_tran_oper_bill
from spmi.time_sensor.time_after_05_30 import time_after_05_30


spmi_dwd__dwd_spmn_tran_oper_bill_base_dt = SparkSqlOperator(
    task_id='spmi_dwd__dwd_spmn_tran_oper_bill_base_dt',
    task_concurrency=1,
    pool_slots=8,
    retries=0,
    master='yarn',
    name='spmi_dwd__dwd_spmn_tran_oper_bill_base_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='spmi/dwd/spmi/dwd_spmn_tran_oper_bill_base_dt/execute.sql',
    driver_memory='4G',
    driver_cores=2,
    executor_cores=6,
    executor_memory='12G',
    email=['lukunming@jtexpress.com','yl_bigdata@yl-scm.com'],
    num_executors=60,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 80,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '4G',  # 堆外内存
          'spark.sql.shuffle.partitions': 1600,
          'spark.io.compression.codec': 'lz4',
          'spark.io.compression.lz4.blockSize': '64k'
          },
    hiveconf={'hive.exec.dynamic.partition': 'true',  # 动态分区
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions': 70,  # 每天生成 20 个分区
              'hive.exec.max.dynamic.partitions.pernode': 70,  # 每天生成 20 个分区
              },
    yarn_queue='pro',
    execution_timeout=timedelta(hours=2),
)
spmi_dwd__dwd_spmn_tran_oper_bill_base_dt << [spmi_ods__spmn_tran_oper_bill,time_after_05_30]


